标题： 用于医学图像分割的相似性记忆先验是您所需的一切 显示英文标题

标题： Similarity Memory Prior is All You Need for Medical Image Segmentation

摘要： 近年来，发现猕猴初级视觉皮层（V1）中的“祖母细胞”可以直接识别具有复杂形状的视觉输入。 这启发我们考察这些细胞在促进医学图像分割研究中的价值。 本文中，我们设计了一种相似性记忆先验网络（Sim-MPNet）用于医学图像分割。 具体来说，我们提出了一种动态记忆权重-损失注意力（DMW-LA），通过原型记忆库中的相似性记忆先验来匹配和记忆医学图像中特定病变或器官的类别特征，从而帮助网络学习类别之间的细微纹理变化。 DMW-LA还通过权重-损失动态（W-LD）更新策略在反向中动态更新相似性记忆先验，有效帮助网络直接提取类别特征。 此外，我们提出了双相似性全局内部增强模块（DS-GIM），通过余弦相似性和欧几里得距离深入探索输入数据特征分布的内部差异。 在四个公开数据集上的大量实验表明，Sim-MPNet的分割性能优于其他最先进的方法。 我们的代码可在 https://github.com/vpsg-research/Sim-MPNet 上获得。 显示英文摘要

摘要： In recent years, it has been found that "grandmother cells" in the primary visual cortex (V1) of macaques can directly recognize visual input with complex shapes. This inspires us to examine the value of these cells in promoting the research of medical image segmentation. In this paper, we design a Similarity Memory Prior Network (Sim-MPNet) for medical image segmentation. Specifically, we propose a Dynamic Memory Weights-Loss Attention (DMW-LA), which matches and remembers the category features of specific lesions or organs in medical images through the similarity memory prior in the prototype memory bank, thus helping the network to learn subtle texture changes between categories. DMW-LA also dynamically updates the similarity memory prior in reverse through Weight-Loss Dynamic (W-LD) update strategy, effectively assisting the network directly extract category features. In addition, we propose the Double-Similarity Global Internal Enhancement Module (DS-GIM) to deeply explore the internal differences in the feature distribution of input data through cosine similarity and euclidean distance. Extensive experiments on four public datasets show that Sim-MPNet has better segmentation performance than other state-of-the-art methods. Our code is available on https://github.com/vpsg-research/Sim-MPNet.